Computational reduction vaccine for covid-19 bin75

ABSTRACT

A system for the rapid development of vaccines or anti-bacterial drugs is required when working with pandemics. The easiest way to formulate these new vaccines is through computational reduction of existing organisms via statistical models. Once vaccine candidates are arrived at through this method, “Super Organisms” containing all of the computationally reducible fragments can then be taken through a Crispr reduction process wherein those computationally reducible fragments are removed. The result is a vaccine candidate which has possible problematic function partially or fully removed. The “neutered” version of the virus can be tested in a lab and in clinical trials for efficacy. This patent covers a vaccine candidate utilizing computationally reducible fragments 75 to 99 base pairs in length; those fragments removed from future Covid-19 Super Organisms either collectively (as in this patent) or individually; as well as the RNA transcripts of those fragments.

BACKGROUND OF THE INVENTION

The present invention focuses on a computational reduction vaccine for Covid-19 with reduction fragments between 75 and 99 base pairs.

A computational reduction vaccine may be defined herein as a vaccine candidate which is arrived at by removing various non-repetitive fragments in a virus or bacteria first computationally, then via Crispr in an a “Super-Organism” (an organism which contains all, or the majority, of of those fragments), and then utilizing the remaining organism as a traditional “live” or “dead” vaccine, which even though marginally computationally reduced, is still recognizable by the human immune system as an invader and therefore provokes a useful immune response. That immune response then shields the recipient from the actual virus going forward.

It is now possible via Python modules such as Numpy (numerical Python) and Biopython (a module specifically designed for computationally manipulating DNA sequences), to analyze in great detail and with great speed thousands, or even millions of sequence records available through the NIH GenBank databases.

Those computational methods are not herein described, but the statistical analysis below will illustrate the efficacy of the method in determining the frequency of various structures, as well as the ability to track those structures though time. It is along those two lines—frequency of appearance, and consistency of appearance, across an entire genetic database that one can derive vaccine candidates computationally.

The traditional way to do this would be to remove each fragment or structure via Crispr one by one and test the resulting organism for problematic function. Once problematic function was discovered, use the resulting live or dead virus could be used as a vaccine. However, in the case of Covid-19, where solutions are demanded in shorter time frames, it is more efficient to simply remove all potential problematic function fragments via various fragment length groups in order to create one or two potential vaccine candidates instead of hundreds. This is the second of two such vaccines.

BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 a table of computational fragment reductions from Covid-19 which are between 75 and 99 base pairs. From left to right, you have the “Bin” size, or size of the fragments; the number of appearances of the fragment across the entire Covid-19 database; the appearance percent of the fragment expressed as a decimal; the Record ID for the reference organism in which the fragment was first found; and the “strip” or fragment (each of which is listed below) which when removed from a Covid-19 Super Organism or Base Organism will give us a vaccine.

FIG. 2 is a SnapGene circular view of Covid-19 sample MT345860.1 from which this vaccine is derived.

FIG. 3 is the same SnapGene circular view of MT345860.1 with the fragments outlined below removed.

DETAILED DESCRIPTION OF THE INVENTION

There are several types of vaccines. This invention introduces a new type of vaccine which is a computationally derived reductive vaccine. A computationally derived reductive vaccine utilizes statistical computation to arrive at a list of fragments which can then be removed from live viruses or bacteria via Crispr to arrive at “neutered” versions which can then form the basis for the vaccine.

Computational reduction in this case may be defined as non-laboratory computational reduction of organisms into fragments, which then can be assessed on the basis of frequency across an entire range of similar organisms as well as computationally tested to confirm that those structures are unique to the virus or bacteria in question. The particulars of the method of discovery for these fragments is proprietary.

What is not proprietary is the statistical analysis of the fragments which are outlined in FIG. 1 and below. In the case of this particular vaccine candidate, the fragments which are included are between 75 and 99 base-pairs and appear in the NIH Covid-19 database greater than 66% of the time. The Covid-19 database “snapshot” from which the fragments in this patent were selected was taken on Jun. 16, 2020 at 5:41 am. That database is available upon request.

The result of this patent is relatively simple. When a “Super Organism” or Covid-19 sample which contains all, or most, of the fragments outlined below is found, that Super Organism can then be genetically modified in a laboratory using Crispr to remove those fragments. Once all those fragments are removed from the organism, it can then be tested to see if problematic function remains. “Problematic function” in the case of Covid-19 is two-fold: functions of the virus which cause high transmissibility rates, and functions of the virus which cause high mortality rates. It may take us years to figure out exactly what those functions are and where they appear exactly on the genetic assay. This patent provides a shortcut by simply removing all of the most likely candidates for those problematic functions by identifying fragments which appear often enough not to be considered mutations (i.e. fragments only appearing in one or two samples).

The scan of the entire database of Covid-19 provides 18 fragments between 75 and 99 base pairs which appear more than 66% of the time across the entire database. These fragments are unique to Covid-19 and cannot be found in any other virus in the NIH GenBank databases. Those 18 fragments are listed below. Each fragment table lists the NIH organism Record ID, number of appearances of that identical fragment in the Covid-19 reference database, appearance percentage expressed as a decimal, and the fragment.

Fragment 1:

Organism: MT259284.1 Number of Appearances across NIH Covid-19 database on Jun. 16,2020: 3929 Percentage Appearance (decimal): 99.77% Fragment: TCTTAAAGATGGCACTTGTGGCTTAGTAGAAGTTGAAAAAGGCGTTTTGC CTCAACTTGAACAGCCCTATGTGTTCATCAA

Fragment 2:

Organism: MT365025.1 Number of Appearances across NIH Covid-19 database on Jun. 16,2020: 3928 Percentage Appearance: 99.75% Fragment: GTATGATTTCGGTGATTTCATACAAACCACGCCAGGTAGTGGAGTTCCTG TTGTAGATTCTTATTATTCATTGTTA

Fragment 3:

Organism: MT365025.1 Number of Appearances across NIH Covid-19 database on Jun. 16,2020: 3926 Percentage Appearance: 99.70% AAATGGCTTATAGGTTTAATGGTATTGGAGTTACACAGAATGTTCTCTAT GAGAACCAAAAATTGATTGCCAACCAATTTAATAGT

Fragment 4:

Organism: MT365025.1 Number of Appearances across NIH Covid-19 database on Jun. 16, 2020: 3926 Percentage Appearance: 99.70% CAACATCTTAAAGATGGCACTTGTGGCTTAGTAGAAGTTGAAAAAGGCGT TTTGCCTCAACTTGAACAGCCCTATGTGTTCATCAA

Fragment 5:

Organism: MT365025.1 Number of Appearances across NIH Covid-19 database on Jun. 16, 2020: 3920 Percentage Appearance: 99.54% GAACCACCTTGTAGGTTTGTTACAGACACACCTAAAGGTCCTAAAGTGAA GTATTTATACTTTATTAAAGGATTAAACAACCTAAATAGAGGTATG

Fragment 6:

Organism: MT470125.1 Number of Appearances across NIH Covid-19 database on Jun. 16, 2020: 3917 Percentage Appearance: 99.47% TTCATCTAAGTGTGTGTGTTCTGTTATTGATTTATTACTTGATGATTTTG TTGAAATAATAAAATCCCAAGATTTATCTGTAGTTTCTAAGGTT

Fragment 7:

Organism: MT365025.1 Number of Appearances across NIH Covid-19 database on Jun. 16, 2020: 3916 Percentage Appearance: 99.44% AGGGTGGTCGCACTATTGCCTTTGGAGGCTGTGTGTTCTCTTATGTTGGT TGCCATAACAAGTGTGCCTATTGGGTTCC

Fragment 8:

Organism: MT539163.1 Number of Appearances across NIH Covid-19 database on Jun. 16, 2020: 3912 Percentage Appearance: 99.34% TTGTTGCGGCAATAGTGTTTATAACACTTTGCTTCACACTCAAAAGAAAG ACAGAATGATTGAACTTTCATTAAT

Fragment 9:

Organism: MT612198.1 Number of Appearances across NIH Covid-19 database on Jun. 16, 2020: 3908 Percentage Appearance: 99.24% TTTAATTGTTACTTTCCTTTACAATCATATGGTTTCCAACCCACTAATGG TGTTGGTTACCAACCATACAGAGTAGTA

Fragment 10:

Organism: MT461654.1 Number of Appearances across NIH Covid-19 database on Jun. 16, 2020: 3904 Percentage Appearance: 99.14% AGGTTTTAATTGTTACTTTCCTTTACAATCATATGGTTTCCAACCCACTA ATGGTGTTGGTTACCAACCATACAGAGTAGTA

Fragment 11:

Organism: MT451733.1 Number of Appearances across NIH Covid-19 database on Jun. 16, 2020: 3902 Percentage Appearance: 99.09% ACACCTTGTAATGGTGTTGAAGGTTTTAATTGTTACTTTCCTTTACAATC ATATGGTTTCCAACCCACTAATGGTGTTGGTTACCAAC

Fragment 12:

Organism: MT576689.1 Number of Appearances across NIH Covid-19 database on Jun. 16, 2020: 3899 Percentage Appearance: 99.01% ACAAGAGGAAGTTCAAGAACTTTACTCTCCAATTTTTCTTATTGTTGCGG CAATAGTGTTTATAACACTTTGCTTCACACTCAAAAGAAAG

Fragment 13:

Organism: MT358637.1 Number of Appearances across NIH Covid-19 database on Jun. 16, 2020: 3886 Percentage Appearance: 98.68% ACCGAAGTTGTAGGAGACATTATACTTAAACCAGCAAATAATAGTTTAAA AATTACAGAAGAGGTTGGCCACACA

Fragment 14:

Organism: MT365025.1 Number of Appearances across NIH Covid-19 database on Jun. 16, 2020: 3833 Percentage Appearance: 98.60% TCTTGAGTGTAATGTGAAAACTACCGAAGTTGTAGGAGACATTATACTTA AACCAGCAAATAATAGTTTAAAAATTACAGAAGAGGTTGGCCACACA

Fragment 15:

Organism: MT459838.1 Number of Appearances across NIH Covid-19 database on Jun. 16, 2020: 3833 Percentage Appearance: 98.60% TTAGGGAATTTGTGTTTAAGAATATTGATGGTTATTTTAAAATATATTCT AAGCACACGCCTATTAATTTAGTGCGT

Fragment 16:

Organism: MT365025.1 Number of Appearances across NIH Covid-19 database on Jun. 16, 2020: 3882 Percentage Appearance: 98.58% CAACCAACAGAATCTATTGTTAGATTTCCTAATATTACAAACTTGTGCCC TTTTGGTGAAGTTTTTAACGCCACCAGATT

Fragment 17:

Organism: MT365025.1 Number of Appearances across NIH Covid-19 database on Jun. 16, 2020: 3868 Percentage Appearance: 98.22% CTCTAAAAGCCCCAAAAGAAATTATCTTCTTAGAGGGAGAAACACTTCCC ACAGAAGTGTTAACAGAGGAAGTTGTCTTGAAA

Fragment 18:

Organism: MT509463.1 Number of Appearances across NIH Covid-19 database on Jun. 16, 2020: 2620 Percentage Appearance: 66.53% TGGTGAGTTTAAATTGGCTTCACATATGTATTGTTCTTTTTACCCTCCAG ATGAGGATGAAGAAGAAGGTGATTGTGAAGAAGAAGAGTTTGAGCC

In creation of the vaccine candidate we can also view that vaccine not only as a reductive entity (a library of removable fragments) which can be manufactured from a variety of possible starting organisms, but also as a complete organism which has potentially been “neutered” of its destructive features.

In order to arrive at that possibility, we must first find a Covid-19 sample which contains all of these structures. Of the 3,898 complete Covid-19 sequences in the Jun. 16, 2020 Covid-19 database, there are 2,417 which contain the above sequences. MT345860.1 is one of those Super Organisms or Base Organisms. When computationally reduced, some fragments overlap, meaning that 1,499 of the 2,417 samples which contain the fragments had the maximum removal rate of 13 of 18 fragments.

So to create a reductive vaccine, computationally those fragments are removed to create the vaccine candidate as shown in this patent's sequence file. The original reference sequence can be downloaded from NIH via the reference MT345860.1. As previously stated, there are also 2,417 other reference candidates which could be used as Super Organisms or Base Organisms for the next generation of vaccines. That list is available upon request.

This application also seeks to cover the RNA transcript of each of the fragments. It may well be that RNA transcript vaccines based on these fragments would be of equal or greater efficacy in triggering a useful immune response.

It should also be noted that these fragments are 75 base pairs or greater, which means a fragment has only a 1 in 1.60 quattuordecillion (4⁷⁵) chance of occurring—in the entire history of the planet. In other words, even at a 66% recurrence rate across the entire Covid-19 genome, these fragments represent viable mathematical targets for vaccines.

This application identifies 18 such fragments.

Claims moved to separate file

Abstract moved to separate file. 

Having described my invention herein, I claim:
 1. The reference fragments as “key fragments” which may be computationally removed collectively or individually from 2,417 Covid-19 “Super Organisms” (as of Jun. 16, 2020) to form potential vaccine candidates without laying claim to the actual fragments as genetic material, only their use, collectively or individually, as the basis for the creation of a new organism or organisms including a “neutered” Covid-19 live or dead virus which may be potentially used safely as a vaccine following Crispr reduction, laboratory testing, and clinical trials.
 2. The newly created organism with these fragments removed as shown in the sequence file which is a vaccine candidate for Covid-19 as well as future refinements of same wherein those fragments may be removed collectively, or individually, to create an effective Covid-19 vaccine from other Covid-19 samples, and which includes future refinements of the vaccine wherein the removal of only one or several of these fragments may be required.
 3. The RNA transcript of each of the fragments described herein which individually or collectively can be utilized in the same manner as in claim 1 without requiring the subtraction of the fragments from a Super Organism for the creation of a live or dead vaccine but rather can be utilized in a kind of “shotgun” approach wherein all fragments are included en masse in each dose of the vaccine, or future refinements of same wherein after significant laboratory testing only one or two RNA transcripts of the fragments may need to be utilized. 